Yutaka Iino

Smart grid ready BEMS adopting model-based HVAC control for energy saving

The newly developed building energy management system (BEMS) is presented. The main feature of the BEMS is the following 2 technologies. (1) The new air-conditioning control technology for the energy saving of buildings. The method is mainly focused on the compatibility of energy savings and comfort. The energy saving is achieved through the twin coil air handling unit that controls room humidity and temperature independently without energy loss and by the optimal operation of HVAC (Heating, Ventilating and air-conditioning) system, manipulating the supplying airflow temperature to the rooms, room temperature and the humidity. The comfort is kept by the index (PMV: Predicted Mean Vote) that is calculated with room temperature, humidity, radiation temperature, wind velocity and so on. In order to avoid large-scale nonlinear programming for optimal conditions, the driving function is introduced. The effectiveness of the HVAC control technology is proved through a building HVAC data and the simulations using the data. (2) Automated demand responding mechanism. The mechanism has function that predicts how much electricity demand is trimmed for a given reward. It also has ability to communicate with community energy management server for finding appropriate balances between electricity demand and generations.

Wireless Sensor Network based Control System - Trade off between sensor power saving and control performance -

This paper is discussed on some new control methodologies for wireless sensor network based closed loop control systems. Such a system is required to save battery energy consumption, which will be achieved by saving communication speed. Then some new control strategies considering communication interval, which corresponds to sampling period of discrete control system, are considered here. The communication saved control problems is defined. Then, some heuristic control methods are proposed. One of them is based on the optimization of a cost function including control performance and communication penally. Two types of control optimization problems are defined, which correspond to state observer problem and state feedback control problem. These wireless sensor network conscious control methods will meet industrial requirements.

Hierarchical MPC supervisory control with sensitivity and controllability monitoring

Model predictive control (MPC) enables to handle multi-objective optimal operations and control in various kinds of process controls. In fact, simultaneous optimization of stability, quality constrains and economical optimality, sometimes it fails into ldquoinfeasiblerdquo solution. Then a supervisory control function in the hierarchical MPC configuration is proposed. It evaluates sensitivity of optimization and controllability in sense of ldquocontrol degrees of freedomrdquo and adjusts the control system structure as multi-input and multi-output closed loop system to be feasible. It realizes a kind of automatic ldquoPlug and Playrdquo function for control input. Some numerical simulations for general blending control in material process are evaluated to show the effectiveness of the proposed method.

Risk based Model Predictive Control with hybrid system structure and its application to Thermal Power Plants

An industrial model predictive control method, which combines usual linear control and sequence control, is reformulated to a hybrid control algorithm. The plant start up and shut down sequence is naturally used in the industrial control method, but has not been analyzed theoretically. In this paper, the thermal power plant optimal load distribution problem is formulated as model predictive control with the constraint condition, and also the embedded start up and shut down optimal control sequence is designed with different objective functions. The new hierarchical control system structure is discussed as a hybrid control system, which can dramatically reduce the calculation effort, in spite of plant non-linearity, constraints and parameter uncertainty. A simulation example applied to a thermal power plant is demonstrated to show the effectiveness of the control method

Model Predictive Control with Multi-Objective Cost Function considering Stabilization and Linear Cost Optimization

Abstract Almost all control problem are essentially multi-objective optimization problems which include such items as closed loop stabilization and economic cost optimization As a dynamic optimizer for the process operation, a new Model Predictive Control (MPC) algorithm based on the multi-objective optimization problem is formulated and analyzed. It uses an extended cost function including both conventional penalty terms for control errors or fluctuations, and newly introduced linear cost terms for output and/or input variables. The effectiveness of the proposed method, from the viewpoint of economic cost, is confirmed with some simulation studies, especially in the case of rapid change of the process operating conditions.

Model Based Extended Wavelet Analysis and its application to fault diagnosis of an elevator system

Abstract A new wavelet analysis method is proposed, that is a combination of the wavelet transform, a nonlinear transformation and a correlation analysis. In this paper, it is called ‘Extended Wavelet Analysis (EWA)’ procedure. Furthermore, the ‘Model Based EWA’ method is proposed in which the nonlinear transformation is based on a model of the diagnosed object. It can analyze a short transient signal and it is effective for the correlation analysis between the wavelet spectrum and arbitrary variables in the object. It was applied to the diagnosis of an elevator system. Some simulation results and experimental results reveal the effectiveness of the EWA procedure.